Clinical trial · Observational
An MRI-Based Study of Intelligent Pathological Subtyping and Grading of Renal Tumors
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This retrospective + prospective, non-interventional study aims to develop and evaluate artificial intelligence methods for the detection, pathological subtyping, and histological grading of renal tumors using magnetic resonance imaging (MRI). Approximately 900 adult patients with available preoperative renal MRI examinations and postoperative pathological results will be included. The pathological findings will be used as the reference standard for model development and evaluation. In addition to MRI data, selected demographic, clinical, and laboratory information may be incorporated to improve model performance. The study will not change participants' diagnosis, treatment, or follow-up, and no additional examinations or interventions will be required. All study data will be de-identified before analysis. The ultimate goal is to develop an MRI-based intelligent diagnostic approach that may assist clinicians in the preoperative assessment and individualized management of patients with renal tumors.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Kidney Neoplasm | Kidney Neoplasm | ONTOLOGY_EXACT | 0.90 |
| Renal Tumor | Kidney Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| MRI-Based Artificial Intelligence Analysis | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Patients With Renal Tumors
- description
- Adult patients with pathologically confirmed renal tumors who underwent preoperative multisequence renal MRI as part of routine clinical care and had available pathological subtype and, when applicable, histological grade information. Existing de-identified MRI, pathological, clinical, and laboratory data were collected for artificial intelligence model development and evaluation. No additional examination, treatment, or study-specific intervention was administered.
- interventionNames
- Diagnostic Test: MRI-Based Artificial Intelligence Analysis
Primary outcomes (2)
- measure
- Accuracy of MRI-Based Artificial Intelligence for Pathological Subtyping of Renal Tumors
- timeFrame
- At completion of model evaluation using retrospective data collected from January 2021 through July 2026 and prospective data collected through December 2026.
- description
- The pathological subtype predicted by the MRI-based artificial intelligence model will be compared with the postoperative pathological diagnosis as the reference standard in the held-out test dataset. Accuracy will be calculated as the number of correctly classified renal tumors divided by the total number of renal tumors evaluated. Classification performance for individual pathological subtypes will also be summarized using sensitivity, specificity, and F1 score, where applicable.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Patients aged 18 years or older. * Patients diagnosed with a renal tumor. * Availability of preoperative renal magnetic resonance imaging examinations. * Availability of a corresponding pathological diagnosis, including pathological subtype and, where applicable, histological grade. * Magnetic resonance images that can be successfully retrieved and are of - - sufficient quality for image analysis. Exclusion Criteria: * Absence of renal magnetic resonance imaging data. * Absence of a corresponding pathological diagnosis or insufficient pathological subtype or grading information. * Magnetic resonance images that cannot be retrieved, opened, or read. * Poor image quality that precludes reliable image annotation or artificial intelligence analysis.
References
Publications (0)
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